Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f27dc690390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f27dc70d710>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name="input_real")
    input_z = tf.placeholder(tf.float32, [None, z_dim], name="input_z")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
drop_rate = 0.2
alpha = 0.15
smooth = 0.2
In [7]:
def LeakyReLU(inputs):
    return tf.maximum(inputs * alpha, inputs)

def xavier_init(size, dtype, partition_info):
    
    in_dim = size[-1]
    xavier_stddev = 1. / tf.sqrt(in_dim * 1.)
    return tf.random_normal(shape=size, stddev=xavier_stddev, dtype=dtype)

def discriminator(images, reuse=False, is_training=True):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope("discriminator", reuse=reuse):

        x1 = tf.layers.conv2d(images, 64, 3, padding="same", activation=None, kernel_initializer=xavier_init)
        x1 = tf.layers.average_pooling2d(x1, 3, strides=2)
        
        relu1 = LeakyReLU(x1)
        relu1 = tf.layers.dropout(relu1, drop_rate)
        
        x2 = tf.layers.conv2d(relu1, 128, 3, padding="same", activation=None, kernel_initializer=xavier_init)
        x2 = tf.layers.average_pooling2d(x2, 3, strides=2)

        bn2 = tf.layers.batch_normalization(x2, training=is_training)
        relu2 = LeakyReLU(bn2)
        relu2 = tf.layers.dropout(relu2, drop_rate)
        
        x3 = tf.layers.conv2d(relu2, 512, 3, padding="same", activation=None, kernel_initializer=xavier_init)
        x3 = tf.layers.average_pooling2d(x3, 3, strides=2)
        
        bn3 = tf.layers.batch_normalization(x3, training=is_training)
        relu3 = LeakyReLU(bn3)
        relu3 = tf.layers.dropout(relu3, drop_rate)
        
        flat = tf.contrib.layers.flatten(relu3)
        output = tf.layers.dense(flat, 1)
    
    return tf.nn.sigmoid(output), output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope("generator", reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = LeakyReLU(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 512, 3, strides=2, padding="same", kernel_initializer=xavier_init)
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = LeakyReLU(x2)
        x2 = tf.layers.dropout(x2, drop_rate, training=is_train)
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 3, strides=2, padding="same", kernel_initializer=xavier_init)
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = LeakyReLU(x3)
        x3 = tf.layers.dropout(x3, drop_rate, training=is_train)
        
        x4 = tf.layers.conv2d_transpose(x3, 64, 3, strides=1, padding="same", kernel_initializer=xavier_init)
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = LeakyReLU(x4)
        x4 = tf.layers.dropout(x4, drop_rate, training=is_train)
        
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=1, padding="same", kernel_initializer=xavier_init)
        
        g = tf.tanh(logits)

    return g


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, batch_size=32):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    D_real_out, D_real_digits = discriminator(input_real, reuse=False)
    G_model = generator(input_z, out_channel_dim, True)
    D_fake_out, D_fake_digits = discriminator(G_model, reuse=True)
    """
    lam = 10
    eps = tf.random_uniform([batch_size, *input_real.get_shape().as_list()[1:]], minval=0., maxval=1.)
    X_inter = eps * input_real + (1 - eps) * G_model
    grad = tf.gradients(discriminator(X_inter, reuse=True)[1], [X_inter])[0]
    
    grad_norm = tf.sqrt(tf.reduce_sum((grad) ** 2, axis=1))
    grad_pen = lam * tf.reduce_mean(grad_norm - 1.) ** 2
    
    tf.identity(grad_pen, name="grad_pen")
    tf.identity(grad_norm, name="grad_norm")
    
    D_loss = tf.reduce_mean(D_fake_digits) - tf.reduce_mean(D_real_digits) + grad_pen
    G_loss = -tf.reduce_mean(D_fake_digits)
    """
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real_digits, 
                                                labels=tf.ones_like(D_real_digits) * (1.0 - smooth))
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_digits,
                                               labels=tf.ones_like(D_fake_digits) * (smooth))
    )
    G_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake_digits,
                                               labels=tf.ones_like(D_fake_digits))
    )
    D_loss = d_loss_real + d_loss_fake
    
    
    #G_loss = -tf.reduce_mean(D_fake_digits)
    #D_loss = tf.reduce_mean(D_fake_digits) - tf.reduce_mean(D_real_digits)
    
    return D_loss, G_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    variables = tf.trainable_variables()
    d_var = [var for var in variables if var.name.startswith('discriminator')]
    g_var = [var for var in variables if var.name.startswith("generator")]
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        D_solver = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss,
                                                                                            var_list=d_var)
        G_solver = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss,
                                                                                            var_list=g_var)

    return D_solver, G_solver


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
losses = []
import time
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, 
          save_folder=None, base_i=0, load_path=None):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], batch_size)
    d_solver, g_solver = model_opt(d_loss, g_loss, lr, beta1)

    tf.contrib.layers.summarize_tensors(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES))
    tf.summary.scalar("d_loss", d_loss)
    tf.summary.scalar("g_loss", g_loss)
    
    merge_all = tf.summary.merge_all()
    if save_folder and not os.path.exists(save_folder):
        os.mkdir(save_folder)
        
    tf.contrib.layers.summaries.summarize_variables()
    global alpha, drop_rate, smooth

    with tf.Session() as sess:
        print("begin train")
        saver = tf.train.Saver()
        if load_path:
            saver.restore(sess, load_path)
        else:
            sess.run(tf.global_variables_initializer())
        
        graph = tf.get_default_graph()
        writer = tf.summary.FileWriter("log/{}/lr={} bet={} smooth={} al={} dr={} zdim={}/6".format(save_path, learning_rate,
                                                                                      beta1,
                                                                                      smooth,
                                                                                      alpha,
                                                                                      drop_rate, 
                                                                                      z_dim), graph)
        begin_time = time.time()
        count = 0
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                batch_images *= 2.0
                
                batch_z = np.random.normal(0., 1., size=(batch_size, z_dim))
                for d_i in range(1):
                    # train discriminator more
                    _ = sess.run(d_solver, feed_dict={input_real: batch_images, 
                                                  input_z: batch_z, 
                                                  lr: learning_rate})
                for d_i in range(6):
                    _ = sess.run(g_solver, feed_dict={
                        input_real: batch_images, 
                        input_z: batch_z,
                        lr: learning_rate
                    })
                train_loss_d, train_loss_g, summary = sess.run([d_loss, g_loss, merge_all], feed_dict={
                    input_z: batch_z, input_real:batch_images
                })
                count += 1
                writer.add_summary(summary, count)
                losses.append((train_loss_d, train_loss_g))
                if count % 50 == 0:
                    interval = (time.time() - begin_time) / 50.
                    begin_time = time.time()
                    print("Epoch {}".format(count),
                          "Each train cost time: {}".format(interval),
                         "Discriminator Loss: {:.4f} ".format(train_loss_d),
                         "Generator Loss: {:.4f}".format(train_loss_g))
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                    showLoss()
                
                if save_folder and count % 300 == 0:

                    saver.save(sess, "{}/lr={} bet={} smooth={} al={} dr={}.ckpt".format(save_folder,
                                                                  learning_rate,
                                                                  beta1,
                                                                  smooth,
                                                                  alpha,
                                                                  drop_rate))
        showLoss()
def showLoss():
    fig, ax = pyplot.subplots()
    loss_ar = np.array(losses)
    pyplot.plot(loss_ar.T[0], label="discriminator", alpha=0.5)
    pyplot.plot(loss_ar.T[1], label="generator", alpha=0.5)
    pyplot.title("Training loss")
    pyplot.legend()
    pyplot.show()

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 128
z_dim = 100
learning_rate = 2e-4
beta1 = 0.45

losses = []
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
save_path = "mnist_save"

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, save_path)
begin train
Epoch 50 Each train cost time: 3.1342885589599607 Discriminator Loss: 1.4260  Generator Loss: 0.5878
Epoch 100 Each train cost time: 3.1146410083770752 Discriminator Loss: 1.5029  Generator Loss: 0.4467
Epoch 150 Each train cost time: 3.126657347679138 Discriminator Loss: 1.4212  Generator Loss: 0.5442
Epoch 200 Each train cost time: 3.195663847923279 Discriminator Loss: 1.4270  Generator Loss: 0.5986
Epoch 250 Each train cost time: 3.137639117240906 Discriminator Loss: 1.4332  Generator Loss: 0.6193
Epoch 300 Each train cost time: 3.119871873855591 Discriminator Loss: 1.4569  Generator Loss: 0.5373
Epoch 350 Each train cost time: 3.1592552280426025 Discriminator Loss: 1.4759  Generator Loss: 0.5098
Epoch 400 Each train cost time: 3.1471190595626832 Discriminator Loss: 1.4809  Generator Loss: 0.5023
Epoch 450 Each train cost time: 3.1062571144104005 Discriminator Loss: 1.5025  Generator Loss: 0.5015
Epoch 500 Each train cost time: 3.1053015995025635 Discriminator Loss: 1.5238  Generator Loss: 0.4989
Epoch 550 Each train cost time: 3.139259629249573 Discriminator Loss: 1.4769  Generator Loss: 0.6420
Epoch 600 Each train cost time: 3.117573466300964 Discriminator Loss: 1.4979  Generator Loss: 0.6017
Epoch 650 Each train cost time: 3.155559525489807 Discriminator Loss: 1.4983  Generator Loss: 0.5401
Epoch 700 Each train cost time: 3.12738618850708 Discriminator Loss: 1.5182  Generator Loss: 0.4560
Epoch 750 Each train cost time: 3.1276445055007933 Discriminator Loss: 1.4851  Generator Loss: 0.6904

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 128
z_dim = 500
learning_rate = 3e-4
beta1 = 0.2

losses = []
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
save_path = "celeba_save"


celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, 
          save_folder=save_path)
""", base_i=8, load_path=save_path+"/7_gan.ckpt"""
begin train
Epoch 50 Each train cost time: 3.7864269542694093 Discriminator Loss: 1.7375  Generator Loss: 0.3533
Epoch 100 Each train cost time: 3.7082439613342286 Discriminator Loss: 1.6327  Generator Loss: 0.4528
Epoch 150 Each train cost time: 3.7175854682922362 Discriminator Loss: 1.5931  Generator Loss: 0.4052
Epoch 200 Each train cost time: 3.7386206912994386 Discriminator Loss: 1.5135  Generator Loss: 0.5300
Epoch 250 Each train cost time: 3.714322166442871 Discriminator Loss: 1.5492  Generator Loss: 0.4540
Epoch 300 Each train cost time: 3.7222429847717287 Discriminator Loss: 1.5051  Generator Loss: 0.4712
Epoch 350 Each train cost time: 3.751862907409668 Discriminator Loss: 1.7086  Generator Loss: 0.2952
Epoch 400 Each train cost time: 3.725668148994446 Discriminator Loss: 1.5762  Generator Loss: 0.5161
Epoch 450 Each train cost time: 3.7359394645690918 Discriminator Loss: 1.6108  Generator Loss: 0.4937
Epoch 500 Each train cost time: 3.7791053199768068 Discriminator Loss: 1.6279  Generator Loss: 0.4325
Epoch 550 Each train cost time: 3.7400695133209227 Discriminator Loss: 1.5873  Generator Loss: 0.4948
Epoch 600 Each train cost time: 3.743034553527832 Discriminator Loss: 1.5276  Generator Loss: 0.5294
Epoch 650 Each train cost time: 3.770116934776306 Discriminator Loss: 1.5412  Generator Loss: 0.5055
Epoch 700 Each train cost time: 3.7336610174179077 Discriminator Loss: 1.5218  Generator Loss: 0.5439
Epoch 750 Each train cost time: 3.7476540756225587 Discriminator Loss: 1.5025  Generator Loss: 0.5601
Epoch 800 Each train cost time: 3.7105571365356447 Discriminator Loss: 1.5223  Generator Loss: 0.5249
Epoch 850 Each train cost time: 3.7155943775177 Discriminator Loss: 1.5209  Generator Loss: 0.5196
Epoch 900 Each train cost time: 3.7110152101516722 Discriminator Loss: 1.4964  Generator Loss: 0.5430
Epoch 950 Each train cost time: 3.7400403738021852 Discriminator Loss: 1.4857  Generator Loss: 0.5651
Epoch 1000 Each train cost time: 3.6762979364395143 Discriminator Loss: 1.4844  Generator Loss: 0.6097
Epoch 1050 Each train cost time: 3.6686793518066407 Discriminator Loss: 1.4953  Generator Loss: 0.5509
Epoch 1100 Each train cost time: 3.701257209777832 Discriminator Loss: 1.4721  Generator Loss: 0.5888
Epoch 1150 Each train cost time: 3.772213592529297 Discriminator Loss: 1.4923  Generator Loss: 0.5525
Epoch 1200 Each train cost time: 3.6864229917526243 Discriminator Loss: 1.4900  Generator Loss: 0.5631
Epoch 1250 Each train cost time: 3.753456697463989 Discriminator Loss: 1.4652  Generator Loss: 0.5897
Epoch 1300 Each train cost time: 3.6959688234329224 Discriminator Loss: 1.4705  Generator Loss: 0.5856
Epoch 1350 Each train cost time: 3.771002478599548 Discriminator Loss: 1.4739  Generator Loss: 0.5617
Epoch 1400 Each train cost time: 3.7124476289749144 Discriminator Loss: 1.4489  Generator Loss: 0.5777
Epoch 1450 Each train cost time: 3.684826903343201 Discriminator Loss: 1.4480  Generator Loss: 0.6290
Epoch 1500 Each train cost time: 3.686590623855591 Discriminator Loss: 1.4661  Generator Loss: 0.5904
Epoch 1550 Each train cost time: 3.7340657758712767 Discriminator Loss: 1.4442  Generator Loss: 0.6020
Out[14]:
', base_i=8, load_path=save_path+"/7_gan.ckpt'

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.